1. Field
Apparatuses and methods consistent with the exemplary embodiments relate to processing a 3 dimensional (3D) image that is captured by optical coherence tomography (OCT), and more particularly, to a method and apparatus for enhancing the quality of a 3D image by denoising the 3D image and enhancing a contrast ratio of the 3D image.
2. Description of the Related Art
Optical coherence tomography (OCT) technology is a high-resolution biological tissue imaging technology that has been developed in the last several decades. Although OCT technology has been successfully used in ophthalmology clinical medicine, it is still incomplete in fields where very dense tissues are photographed, for example, in a tumor test and a skin disease test. Since very dense tissues highly disperse light and thus lower invasiveness, a signal-to-noise ratio of an OCT system and a dynamic range of a generated image are greatly degraded so that the interpretation and diagnosis of organs and lesions in an image are difficult. In order to enhance a resolution of an image of very dense tissues, image denoising must be performed quickly and efficiently and a contrast ratio in a detailed part of an image must be enhanced.
Noise in an OCT image mainly includes additive noise and multiplicative noise which are also called non-coherent noise and coherent noise. A denoising method is mainly divided into denoising based on hardware and denoising based on digital filtering. The hardware-based denoising is a mixed technology primarily of a space, a frequency, an angle, and a polarization state. In an ideal environment, additive noise and multiplicative noise of a system may be physically reduced by such a mixed technology. However, since additional system hardware is needed, manufacturing costs of the system may be increased. Also, since the mixed technology needs separate scanning and image averaging processes, a system imaging speed and an image contrast ratio are remarkably lowered. In comparison with the above, a software-based real-time post-treatment method may denoise an image and enhance a contrast ratio without affecting an imaging speed. Thus, the software-based real-time post-treatment method is a superior method that may replace the hardware-based processing method.
Most existing denoising and image enhancing methods are applied to 2D images and also suggest different denoising algorithms and filters mainly based on reasonable noise models. The filter is mainly classified into four (4) types: a linear filter, a non-linear filter, a diffusion filter, and a multi-scale analysis based filter. The four existing filters all may reduce noise. However, when the filter is applied to an OCT image, there are limitations: (1) a filter with a relatively superior effect does not satisfy the requirement of an OCT system of performing processing in real time at a high speed, (2) some particular noise in an OCT image is not processed due to limits of a filter model which is used, and (3) a filtered image is unclear so that details of the filtered image are not clearly displayed.
Recently, an algorithm for reducing some noise of a 3D OCT image has been developed. To a degree, this solves the defect where an image is made unclear in a process of filtering using a 2D filter. An averaging method mainly includes an averaging method based on kinetic compensation and an averaging method based on a multi-scale wavelet analysis. According to an article entitled “Sparsity Based Denoising of Spectral Domain Optical Coherence Tomography Images,” by L. fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, a method of denoising a 3D OCT image based on sparsity is suggested and thus an unclear image according to the averaging method may be further enhanced. However, the processing speed of the 3D denoising method may not satisfy actual processing requirements.
A general image is obtained by using a charge coupled device (CCD) or a photosensitive film and noise characteristics of pixels of the general image are the same. However, an OCT image is obtained by individually scanning a point or a line and noise characteristics of each point is very closely related to a state of a system at a fixed time point and a state of a scanned object. Accordingly, a current method of denoising a 3D OCT image may not effectively reduce various noises having different noise characteristics. To summarize, current image denoising methods have at least the following problems: (1) A 2D filter and a 3D filter having a relatively superior effect do not satisfy the requirements of an OCT system which operates at a high speed and in real time, (2) noise having different noise characteristics from a particular noise in an OCT image is not processed; and (3) an image that is a result of filtering is not interpreted in detail because details of the image are unclear.